Fiber density estimation from single q-shell diffusion imaging by tensor divergence
Fiber density estimation from single q-shell diffusion imaging by tensor divergence
复制标题
通过张量散度从单 q 壳扩散成像估计纤维密度
DOI：
10.1016/j.neuroimage.2013.03.032
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发表时间：
2013
期刊：
影响因子：
5.7
通讯作者：
Valerij G. Kiselev
中科院分区：
文献类型：
--
作者：
Marco Reisert;Irina Mader;Roza Umarova;Simon Maier;Ludger Tebartz van Elst;Valerij G. Kiselev
弥散加权磁共振成像提供了有关人脑神经纤维束几何形状的信息。虽然基本纤维束取向的推断仅需要单个q壳层测量，但相对于测量技术和分析，其体积分数的绝对确定更具挑战性。不幸的是，通常采用的多室模型不能适用于单q壳测量，因为室的扩散系数不能解决。这项工作提出了一个方程的纤维取向密度，可以推断的绝对分数的一个全球性的因素。该方程受流体动力学中经典质量守恒定律的启发，表达了与纤维不终止于白色物质的假设相关的纤维守恒。对合成体的模拟表明，该方法能够正确地导出各种构型的密度。使用伪地面真实幻影的实验表明，即使对于复杂的、类似大脑的几何形状，该方法也能够正确地推断密度。81名健康志愿者的体内结果合理且一致。年龄和性别方面的组分析显示出显着的差异，因此，拟议的地图可以用作组和纵向分析的定量措施。
Diffusion-weighted magnetic resonance imaging provides information about the nerve fiber bundle geometry of the human brain. While the inference of the underlying fiber bundle orientation only requires single q-shell measurements, the absolute determination of their volume fractions is much more challenging with respect to measurement techniques and analysis. Unfortunately, the usually employed multi-compartment models cannot be applied to single q-shell measurements, because the compartment's diffusivities cannot be resolved. This work proposes an equation for fiber orientation densities that can infer the absolute fraction up to a global factor. This equation, which is inspired by the classical mass preservation law in fluid dynamics, expresses the fiber conservation associated with the assumption that fibers do not terminate in white matter. Simulations on synthetic phantoms show that the approach is able to derive the densities correctly for various configurations. Experiments with a pseudo ground truth phantom show that even for complex, brain-like geometries the method is able to infer the densities correctly. In-vivo results with 81 healthy volunteers are plausible and consistent. A group analysis with respect to age and gender show significant differences, such that the proposed maps can be used as a quantitative measure for group and longitudinal analysis.
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影响因子：
3.3
作者：
Tuch, DS
通讯作者：
Tuch, DS
影响因子：
10.6
作者：
Reisert, Marco;Kiselev, Valerij G.
通讯作者：
Kiselev, Valerij G.
通过张量散度估计纤维密度
DOI：
10.1007/978-3-642-33418-4_37
发表时间：
2012
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Marco Reisert;Henrik Skibbe;Valerij G. Kiselev
通讯作者：
Valerij G. Kiselev
影响因子：
5.7
作者：
Reisert, Marco;Mader, Irina;Kiselev, Valerij
通讯作者：
Kiselev, Valerij